Overview

Dataset statistics

Number of variables13
Number of observations194
Missing cells330
Missing cells (%)13.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 KiB
Average record size in memory104.7 B

Variable types

Categorical2
Numeric11

Alerts

Country has a high cardinality: 194 distinct values High cardinality
Under15 is highly correlated with Over60 and 8 other fieldsHigh correlation
Over60 is highly correlated with Under15 and 8 other fieldsHigh correlation
FertilityRate is highly correlated with Under15 and 8 other fieldsHigh correlation
LifeExpectancy is highly correlated with Under15 and 8 other fieldsHigh correlation
ChildMortality is highly correlated with Under15 and 8 other fieldsHigh correlation
CellularSubscribers is highly correlated with Under15 and 8 other fieldsHigh correlation
LiteracyRate is highly correlated with Under15 and 8 other fieldsHigh correlation
GNI is highly correlated with Under15 and 8 other fieldsHigh correlation
PrimarySchoolEnrollmentMale is highly correlated with Under15 and 8 other fieldsHigh correlation
PrimarySchoolEnrollmentFemale is highly correlated with Under15 and 8 other fieldsHigh correlation
Under15 is highly correlated with Over60 and 8 other fieldsHigh correlation
Over60 is highly correlated with Under15 and 6 other fieldsHigh correlation
FertilityRate is highly correlated with Under15 and 8 other fieldsHigh correlation
LifeExpectancy is highly correlated with Under15 and 8 other fieldsHigh correlation
ChildMortality is highly correlated with Under15 and 8 other fieldsHigh correlation
CellularSubscribers is highly correlated with Under15 and 7 other fieldsHigh correlation
LiteracyRate is highly correlated with Under15 and 7 other fieldsHigh correlation
GNI is highly correlated with Under15 and 5 other fieldsHigh correlation
PrimarySchoolEnrollmentMale is highly correlated with Under15 and 6 other fieldsHigh correlation
PrimarySchoolEnrollmentFemale is highly correlated with Under15 and 7 other fieldsHigh correlation
Under15 is highly correlated with Over60 and 5 other fieldsHigh correlation
Over60 is highly correlated with Under15 and 5 other fieldsHigh correlation
FertilityRate is highly correlated with Under15 and 5 other fieldsHigh correlation
LifeExpectancy is highly correlated with Under15 and 5 other fieldsHigh correlation
ChildMortality is highly correlated with Under15 and 6 other fieldsHigh correlation
CellularSubscribers is highly correlated with GNIHigh correlation
LiteracyRate is highly correlated with Under15 and 4 other fieldsHigh correlation
GNI is highly correlated with Under15 and 7 other fieldsHigh correlation
PrimarySchoolEnrollmentMale is highly correlated with PrimarySchoolEnrollmentFemaleHigh correlation
PrimarySchoolEnrollmentFemale is highly correlated with LifeExpectancy and 3 other fieldsHigh correlation
Region is highly correlated with Under15 and 8 other fieldsHigh correlation
Population is highly correlated with PrimarySchoolEnrollmentFemaleHigh correlation
Under15 is highly correlated with Region and 7 other fieldsHigh correlation
Over60 is highly correlated with Region and 6 other fieldsHigh correlation
FertilityRate is highly correlated with Region and 8 other fieldsHigh correlation
LifeExpectancy is highly correlated with Region and 9 other fieldsHigh correlation
ChildMortality is highly correlated with Region and 8 other fieldsHigh correlation
CellularSubscribers is highly correlated with Region and 7 other fieldsHigh correlation
LiteracyRate is highly correlated with Region and 6 other fieldsHigh correlation
GNI is highly correlated with Region and 3 other fieldsHigh correlation
PrimarySchoolEnrollmentMale is highly correlated with FertilityRate and 5 other fieldsHigh correlation
PrimarySchoolEnrollmentFemale is highly correlated with Region and 7 other fieldsHigh correlation
FertilityRate has 11 (5.7%) missing values Missing
CellularSubscribers has 10 (5.2%) missing values Missing
LiteracyRate has 91 (46.9%) missing values Missing
GNI has 32 (16.5%) missing values Missing
PrimarySchoolEnrollmentMale has 93 (47.9%) missing values Missing
PrimarySchoolEnrollmentFemale has 93 (47.9%) missing values Missing
Country is uniformly distributed Uniform
Country has unique values Unique

Reproduction

Analysis started2022-04-16 20:47:12.508486
Analysis finished2022-04-16 20:47:41.865273
Duration29.36 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct194
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Afghanistan
 
1
Saint Vincent and the Grenadines
 
1
Niger
 
1
Nigeria
 
1
Niue
 
1
Other values (189)
189 

Length

Max length41
Median length7
Mean length9.819587629
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.5%
Saint Vincent and the Grenadines1
 
0.5%
Niger1
 
0.5%
Nigeria1
 
0.5%
Niue1
 
0.5%
Norway1
 
0.5%
Oman1
 
0.5%
Pakistan1
 
0.5%
Palau1
 
0.5%
Panama1
 
0.5%
Other values (184)184
94.8%

Length

2022-04-16T22:47:42.014876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic13
 
4.7%
of11
 
3.9%
and6
 
2.2%
united4
 
1.4%
the3
 
1.1%
democratic3
 
1.1%
saint3
 
1.1%
islands3
 
1.1%
guinea3
 
1.1%
people's2
 
0.7%
Other values (221)228
81.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Region
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Europe
53 
Africa
46 
Americas
35 
Western Pacific
27 
Eastern Mediterranean
22 

Length

Max length21
Median length6
Mean length9.824742268
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastern Mediterranean
2nd rowEurope
3rd rowAfrica
4th rowEurope
5th rowAfrica

Common Values

ValueCountFrequency (%)
Europe53
27.3%
Africa46
23.7%
Americas35
18.0%
Western Pacific27
13.9%
Eastern Mediterranean22
11.3%
South-East Asia11
 
5.7%

Length

2022-04-16T22:47:42.185486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-16T22:47:42.283109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
europe53
20.9%
africa46
18.1%
americas35
13.8%
western27
10.6%
pacific27
10.6%
eastern22
8.7%
mediterranean22
8.7%
south-east11
 
4.3%
asia11
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Population
Real number (ℝ≥0)

HIGH CORRELATION

Distinct191
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36359.97423
Minimum1
Maximum1390000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:42.424369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75.9
Q11695.75
median7790
Q324535.25
95-th percentile123100
Maximum1390000
Range1389999
Interquartile range (IQR)22839.5

Descriptive statistics

Standard deviation137903.1412
Coefficient of variation (CV)3.792718344
Kurtosis77.74529006
Mean36359.97423
Median Absolute Deviation (MAD)7260.5
Skewness8.516265354
Sum7053835
Variance1.901727636 × 1010
MonotonicityNot monotonic
2022-04-16T22:47:42.589349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102
 
1.0%
1052
 
1.0%
212
 
1.0%
298251
 
0.5%
66871
 
0.5%
1690001
 
0.5%
11
 
0.5%
49941
 
0.5%
33141
 
0.5%
1790001
 
0.5%
Other values (181)181
93.3%
ValueCountFrequency (%)
11
0.5%
102
1.0%
212
1.0%
311
0.5%
381
0.5%
531
0.5%
541
0.5%
721
0.5%
781
0.5%
891
0.5%
ValueCountFrequency (%)
13900001
0.5%
12400001
0.5%
3180001
0.5%
2470001
0.5%
1990001
0.5%
1790001
0.5%
1690001
0.5%
1550001
0.5%
1430001
0.5%
1270001
0.5%

Under15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct179
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.73242268
Minimum13.12
Maximum49.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:42.992698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13.12
5-th percentile14.5425
Q118.7175
median28.65
Q337.7525
95-th percentile45.744
Maximum49.99
Range36.87
Interquartile range (IQR)19.035

Descriptive statistics

Standard deviation10.53457332
Coefficient of variation (CV)0.3666441023
Kurtosis-1.184848269
Mean28.73242268
Median Absolute Deviation (MAD)9.68
Skewness0.20895134
Sum5574.09
Variance110.977235
MonotonicityNot monotonic
2022-04-16T22:47:43.420331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.14
 
2.1%
30.613
 
1.5%
25.963
 
1.5%
42.372
 
1.0%
41.482
 
1.0%
15.22
 
1.0%
35.352
 
1.0%
14.042
 
1.0%
28.652
 
1.0%
18.262
 
1.0%
Other values (169)170
87.6%
ValueCountFrequency (%)
13.121
0.5%
13.171
0.5%
13.281
0.5%
13.531
0.5%
14.042
1.0%
14.161
0.5%
14.181
0.5%
14.411
0.5%
14.511
0.5%
14.561
0.5%
ValueCountFrequency (%)
49.991
0.5%
48.541
0.5%
48.521
0.5%
47.581
0.5%
47.421
0.5%
47.351
0.5%
47.141
0.5%
46.731
0.5%
46.331
0.5%
45.91
0.5%

Over60
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct174
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.16365979
Minimum0.81
Maximum31.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:44.143788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.81
5-th percentile3.8595
Q15.2
median8.53
Q316.6875
95-th percentile24.2925
Maximum31.92
Range31.11
Interquartile range (IQR)11.4875

Descriptive statistics

Standard deviation7.149330525
Coefficient of variation (CV)0.6404109993
Kurtosis-0.5289511144
Mean11.16365979
Median Absolute Deviation (MAD)3.795
Skewness0.8608790582
Sum2165.75
Variance51.11292695
MonotonicityNot monotonic
2022-04-16T22:47:44.337989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.844
 
2.1%
12.353
 
1.5%
9.073
 
1.5%
19.312
 
1.0%
26.972
 
1.0%
3.722
 
1.0%
23.822
 
1.0%
18.62
 
1.0%
5.12
 
1.0%
3.82
 
1.0%
Other values (164)170
87.6%
ValueCountFrequency (%)
0.811
0.5%
1.731
0.5%
3.381
0.5%
3.722
1.0%
3.731
0.5%
3.82
1.0%
3.821
0.5%
3.841
0.5%
3.871
0.5%
3.881
0.5%
ValueCountFrequency (%)
31.921
0.5%
26.972
1.0%
26.721
0.5%
26.111
0.5%
25.91
0.5%
25.411
0.5%
25.321
0.5%
24.691
0.5%
24.391
0.5%
24.241
0.5%

FertilityRate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct143
Distinct (%)78.1%
Missing11
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2.940655738
Minimum1.26
Maximum7.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:44.820695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.26
5-th percentile1.39
Q11.835
median2.4
Q33.905
95-th percentile5.997
Maximum7.58
Range6.32
Interquartile range (IQR)2.07

Descriptive statistics

Standard deviation1.480984454
Coefficient of variation (CV)0.5036238805
Kurtosis-0.01406535381
Mean2.940655738
Median Absolute Deviation (MAD)0.8
Skewness0.9945293518
Sum538.14
Variance2.193314952
MonotonicityNot monotonic
2022-04-16T22:47:45.008329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.515
 
2.6%
1.474
 
2.1%
1.843
 
1.5%
2.383
 
1.5%
1.393
 
1.5%
1.373
 
1.5%
23
 
1.5%
2.312
 
1.0%
1.452
 
1.0%
1.932
 
1.0%
Other values (133)153
78.9%
(Missing)11
 
5.7%
ValueCountFrequency (%)
1.261
 
0.5%
1.271
 
0.5%
1.291
 
0.5%
1.331
 
0.5%
1.373
1.5%
1.381
 
0.5%
1.393
1.5%
1.41
 
0.5%
1.431
 
0.5%
1.442
1.0%
ValueCountFrequency (%)
7.581
0.5%
6.851
0.5%
6.771
0.5%
6.491
0.5%
6.211
0.5%
6.151
0.5%
6.111
0.5%
6.11
0.5%
6.061
0.5%
6.021
0.5%

LifeExpectancy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.01030928
Minimum47
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:45.187112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile52.3
Q164
median72.5
Q376
95-th percentile82
Maximum83
Range36
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.259074636
Coefficient of variation (CV)0.1322530172
Kurtosis-0.5041574409
Mean70.01030928
Median Absolute Deviation (MAD)5.5
Skewness-0.6720552116
Sum13582
Variance85.73046312
MonotonicityNot monotonic
2022-04-16T22:47:45.351985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
7419
 
9.8%
8213
 
6.7%
7512
 
6.2%
7612
 
6.2%
7210
 
5.2%
8110
 
5.2%
698
 
4.1%
717
 
3.6%
777
 
3.6%
736
 
3.1%
Other values (26)90
46.4%
ValueCountFrequency (%)
471
 
0.5%
481
 
0.5%
491
 
0.5%
504
2.1%
513
1.5%
534
2.1%
543
1.5%
552
 
1.0%
565
2.6%
571
 
0.5%
ValueCountFrequency (%)
833
 
1.5%
8213
6.7%
8110
5.2%
806
 
3.1%
795
 
2.6%
784
 
2.1%
777
 
3.6%
7612
6.2%
7512
6.2%
7419
9.8%

ChildMortality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct170
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.14896907
Minimum2.2
Maximum181.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:45.604635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.265
Q18.425
median18.6
Q355.975
95-th percentile109.665
Maximum181.6
Range179.4
Interquartile range (IQR)47.55

Descriptive statistics

Standard deviation37.99293531
Coefficient of variation (CV)1.051010203
Kurtosis1.613925529
Mean36.14896907
Median Absolute Deviation (MAD)14
Skewness1.459737121
Sum7012.9
Variance1443.463134
MonotonicityNot monotonic
2022-04-16T22:47:45.785103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.84
 
2.1%
4.13
 
1.5%
17.63
 
1.5%
2.93
 
1.5%
35.22
 
1.0%
4.82
 
1.0%
44.62
 
1.0%
31.12
 
1.0%
4.22
 
1.0%
9.62
 
1.0%
Other values (160)169
87.1%
ValueCountFrequency (%)
2.21
 
0.5%
2.31
 
0.5%
2.81
 
0.5%
2.93
1.5%
31
 
0.5%
3.11
 
0.5%
3.22
1.0%
3.31
 
0.5%
3.62
1.0%
3.71
 
0.5%
ValueCountFrequency (%)
181.61
0.5%
163.51
0.5%
149.81
0.5%
147.41
0.5%
145.71
0.5%
129.11
0.5%
128.61
0.5%
1281
0.5%
123.71
0.5%
113.51
0.5%

CellularSubscribers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct180
Distinct (%)97.8%
Missing10
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean93.64152174
Minimum2.57
Maximum196.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:45.946074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.57
5-th percentile22.444
Q163.5675
median97.745
Q3120.805
95-th percentile163.1065
Maximum196.41
Range193.84
Interquartile range (IQR)57.2375

Descriptive statistics

Standard deviation41.40044713
Coefficient of variation (CV)0.4421163429
Kurtosis-0.3302750912
Mean93.64152174
Median Absolute Deviation (MAD)27.795
Skewness-0.02140474598
Sum17230.04
Variance1713.997023
MonotonicityNot monotonic
2022-04-16T22:47:46.108446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.392
 
1.0%
48.382
 
1.0%
40.652
 
1.0%
86.062
 
1.0%
130.971
 
0.5%
168.971
 
0.5%
61.611
 
0.5%
74.941
 
0.5%
188.61
 
0.5%
34.221
 
0.5%
Other values (170)170
87.6%
(Missing)10
 
5.2%
ValueCountFrequency (%)
2.571
0.5%
4.091
0.5%
4.471
0.5%
6.851
0.5%
11.691
0.5%
13.641
0.5%
16.671
0.5%
21.321
0.5%
21.631
0.5%
22.331
0.5%
ValueCountFrequency (%)
196.411
0.5%
191.241
0.5%
188.61
0.5%
179.311
0.5%
178.881
0.5%
175.091
0.5%
168.971
0.5%
166.021
0.5%
165.721
0.5%
164.021
0.5%

LiteracyRate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct79
Distinct (%)76.7%
Missing91
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean83.71067961
Minimum31.1
Maximum99.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:46.279291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum31.1
5-th percentile50.42
Q171.6
median91.8
Q397.85
95-th percentile99.7
Maximum99.8
Range68.7
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation17.53064495
Coefficient of variation (CV)0.2094194555
Kurtosis0.3900443642
Mean83.71067961
Median Absolute Deviation (MAD)7.3
Skewness-1.148555414
Sum8622.2
Variance307.3235123
MonotonicityNot monotonic
2022-04-16T22:47:46.441595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.76
 
3.1%
99.63
 
1.5%
99.83
 
1.5%
98.83
 
1.5%
73.22
 
1.0%
89.22
 
1.0%
87.42
 
1.0%
86.62
 
1.0%
97.72
 
1.0%
93.92
 
1.0%
Other values (69)76
39.2%
(Missing)91
46.9%
ValueCountFrequency (%)
31.11
0.5%
34.51
0.5%
411
0.5%
42.11
0.5%
42.41
0.5%
501
0.5%
54.21
0.5%
561
0.5%
56.11
0.5%
56.21
0.5%
ValueCountFrequency (%)
99.83
1.5%
99.76
3.1%
99.63
1.5%
99.51
 
0.5%
99.41
 
0.5%
992
 
1.0%
98.91
 
0.5%
98.83
1.5%
98.51
 
0.5%
98.41
 
0.5%

GNI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct157
Distinct (%)96.9%
Missing32
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean13320.92593
Minimum340
Maximum86440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:46.606048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum340
5-th percentile951
Q12335
median7870
Q317557.5
95-th percentile42192.5
Maximum86440
Range86100
Interquartile range (IQR)15222.5

Descriptive statistics

Standard deviation15192.98865
Coefficient of variation (CV)1.14053548
Kurtosis3.993813093
Mean13320.92593
Median Absolute Deviation (MAD)6205
Skewness1.874357239
Sum2157990
Variance230826904.1
MonotonicityNot monotonic
2022-04-16T22:47:46.759611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19402
 
1.0%
10402
 
1.0%
17102
 
1.0%
59302
 
1.0%
11102
 
1.0%
204301
 
0.5%
28701
 
0.5%
110801
 
0.5%
145101
 
0.5%
25701
 
0.5%
Other values (147)147
75.8%
(Missing)32
 
16.5%
ValueCountFrequency (%)
3401
0.5%
5401
0.5%
5801
0.5%
6101
0.5%
7201
0.5%
8101
0.5%
8401
0.5%
8701
0.5%
9501
0.5%
9701
0.5%
ValueCountFrequency (%)
864401
0.5%
642601
0.5%
614601
0.5%
593801
0.5%
525701
0.5%
488201
0.5%
478901
0.5%
431401
0.5%
422001
0.5%
420501
0.5%

PrimarySchoolEnrollmentMale
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct75
Distinct (%)74.3%
Missing93
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean90.85049505
Minimum37.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:46.912057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37.2
5-th percentile68.2
Q187.7
median94.7
Q398.1
95-th percentile99.6
Maximum100
Range62.8
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation11.01714685
Coefficient of variation (CV)0.1212667784
Kurtosis6.182613866
Mean90.85049505
Median Absolute Deviation (MAD)4.2
Skewness-2.250329347
Sum9175.9
Variance121.3775248
MonotonicityNot monotonic
2022-04-16T22:47:47.060843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.15
 
2.6%
97.74
 
2.1%
99.33
 
1.5%
94.83
 
1.5%
99.63
 
1.5%
93.32
 
1.0%
98.82
 
1.0%
99.52
 
1.0%
96.92
 
1.0%
97.82
 
1.0%
Other values (65)73
37.6%
(Missing)93
47.9%
ValueCountFrequency (%)
37.21
0.5%
56.51
0.5%
60.11
0.5%
60.71
0.5%
64.21
0.5%
68.21
0.5%
70.61
0.5%
72.21
0.5%
72.81
0.5%
75.91
0.5%
ValueCountFrequency (%)
1001
 
0.5%
99.81
 
0.5%
99.72
 
1.0%
99.63
1.5%
99.52
 
1.0%
99.41
 
0.5%
99.33
1.5%
99.21
 
0.5%
99.15
2.6%
98.92
 
1.0%

PrimarySchoolEnrollmentFemale
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct77
Distinct (%)76.2%
Missing93
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean89.63267327
Minimum32.5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-04-16T22:47:47.227077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum32.5
5-th percentile60.6
Q187.3
median95.1
Q397.9
95-th percentile99.7
Maximum100
Range67.5
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation12.81761373
Coefficient of variation (CV)0.1430015782
Kurtosis4.47694865
Mean89.63267327
Median Absolute Deviation (MAD)4.1
Skewness-2.048405524
Sum9052.9
Variance164.2912218
MonotonicityNot monotonic
2022-04-16T22:47:47.379919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.24
 
2.1%
974
 
2.1%
94.43
 
1.5%
98.53
 
1.5%
91.53
 
1.5%
99.73
 
1.5%
99.33
 
1.5%
87.32
 
1.0%
99.62
 
1.0%
96.52
 
1.0%
Other values (67)72
37.1%
(Missing)93
47.9%
ValueCountFrequency (%)
32.51
0.5%
521
0.5%
54.81
0.5%
55.91
0.5%
561
0.5%
60.61
0.5%
60.81
0.5%
66.51
0.5%
70.41
0.5%
70.51
0.5%
ValueCountFrequency (%)
1001
 
0.5%
99.91
 
0.5%
99.81
 
0.5%
99.73
1.5%
99.62
1.0%
99.52
1.0%
99.33
1.5%
99.24
2.1%
991
 
0.5%
98.53
1.5%

Interactions

2022-04-16T22:47:38.729475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:17.545128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:20.116302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:22.786877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:25.143122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:27.515715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:29.457618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:31.497931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:33.425632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:35.436250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:36.986414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:38.886587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:17.760924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:20.371729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:22.973912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:25.332027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:27.708603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:29.621442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:31.668502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:33.608901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:35.584470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:37.137939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:39.095114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:18.000284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:20.625243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:23.161040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:25.508992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:27.860909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:29.958522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:31.815622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:33.769446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:35.733883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:37.289877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:39.250109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:18.356331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:20.886113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:23.428594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:25.658746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:27.997939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:30.104432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:31.973159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:33.915280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:35.866452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:37.409488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:39.585833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:18.635522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:21.137470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:23.664679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:25.807582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:28.199677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:30.256580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:32.163864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:34.062044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:35.991091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:37.557323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:39.727560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:18.850947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:21.439793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:23.842615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:25.968404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:28.470748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:30.405788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:32.321197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:34.216955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:36.141453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:37.713306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:39.953751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:19.044956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:21.674950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:24.219798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:26.114148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:28.648447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:30.670343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:32.471358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:34.356661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:36.271032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:37.853245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:40.119715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:19.273859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:21.941192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:24.391530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:26.577631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:28.809209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:30.833435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:32.640949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:34.634166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:36.423278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:38.008064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:40.278621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:19.470335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:22.239464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:24.527562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:26.931726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:28.968498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:31.004229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:32.806604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:34.990131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:36.575361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:38.163322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:40.414726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:19.670800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:22.402101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:24.705322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:27.086358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:29.125871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:31.156204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:32.947847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:35.133935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:36.690350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:38.395609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:40.678437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:19.900185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:22.604362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:24.909061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:27.278158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:29.296411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:31.332747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:33.119220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:35.281950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:36.838332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-16T22:47:38.566620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-16T22:47:47.530584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-16T22:47:47.917719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-16T22:47:48.158984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-16T22:47:48.530249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-16T22:47:40.986228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-16T22:47:41.295670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-16T22:47:41.551912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-16T22:47:41.740999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CountryRegionPopulationUnder15Over60FertilityRateLifeExpectancyChildMortalityCellularSubscribersLiteracyRateGNIPrimarySchoolEnrollmentMalePrimarySchoolEnrollmentFemale
0AfghanistanEastern Mediterranean2982547.423.825.406098.554.26NaN1140.0NaNNaN
1AlbaniaEurope316221.3314.931.757416.796.39NaN8820.0NaNNaN
2AlgeriaAfrica3848227.427.172.837320.098.99NaN8310.098.296.4
3AndorraEurope7815.2022.86NaN823.275.49NaNNaN78.479.4
4AngolaAfrica2082147.583.846.1051163.548.3870.15230.093.178.2
5Antigua and BarbudaAmericas8925.9612.352.12759.9196.4199.017900.091.184.5
6ArgentinaAmericas4108724.4214.972.207614.2134.9297.817130.0NaNNaN
7ArmeniaEurope296920.3414.061.747116.4103.5799.66100.0NaNNaN
8AustraliaWestern Pacific2305018.9519.461.89824.9108.34NaN38110.096.997.5
9AustriaEurope846414.5123.521.44814.0154.78NaN42050.0NaNNaN

Last rows

CountryRegionPopulationUnder15Over60FertilityRateLifeExpectancyChildMortalityCellularSubscribersLiteracyRateGNIPrimarySchoolEnrollmentMalePrimarySchoolEnrollmentFemale
184United Republic of TanzaniaAfrica4778344.854.895.365954.055.5373.21500.0NaNNaN
185United States of AmericaAmericas31800019.6319.312.00797.192.72NaN48820.095.496.1
186UruguayAmericas339522.0518.592.07777.2140.7598.114640.0NaNNaN
187UzbekistanEurope2854128.906.382.386839.691.6599.43420.093.391.0
188VanuatuWestern Pacific24737.376.023.467217.955.7682.64330.0NaNNaN
189Venezuela (Bolivarian Republic of)Americas2995528.849.172.447515.397.78NaN12430.094.795.1
190Viet NamWestern Pacific9079622.879.321.797523.0143.3993.23250.0NaNNaN
191YemenEastern Mediterranean2385240.724.544.356460.047.0563.92170.085.570.5
192ZambiaAfrica1407546.733.955.775588.560.5971.21490.091.493.9
193ZimbabweAfrica1372440.245.683.645489.872.1392.2NaNNaNNaN